AI 中文总结
该研究采用Ta₂O₅非易失性忆阻器作为动态储备池计算层，通过可调遗忘动力学实现高速时间序列预测，仅需2至6个忆阻器通道即可达到高预测精度，工作频率可从kHz提升至MHz范围。
AI 中文摘要
Ta₂O₅非易失性忆阻器被用作紧凑、可追踪且可控的动态储备池计算层，以执行时间序列预测任务。其与电压强相关的切换速度被用于信息处理，可根据正负驱动电压脉冲配置可定制的编程时间与遗忘时间。在时间序列预测问题上对该框架进行基准测试后发现，可配置的遗忘动力学能在使用相当少的忆阻器输入通道时实现高预测精度。训练方式分为两种：一是采用固定遗忘时间的线性回归优化输出层，二是同时优化遗忘时间。前者使用6个忆阻器通道，后者仅用2个忆阻器通道，即可在基准任务中展现出色的预测精度。该方案可将工作频率调谐多个数量级：通过调整输入电压电平，同一忆阻器动态层的信息处理速度可从千赫兹（kHz）提升至兆赫兹（MHz）范围，同时保持优异的预测精度。这些发现证明了基于忆阻器的动态网络在快速时间信号分析、预测与恢复方面的优势，可接近电信数据速率。
英文摘要
Ta$_2$O$_5$ nonvolatile memristors are used as compact, traceable, and well-controllable dynamic reservoir computing layers to perform time-series prediction tasks. The strongly voltage-dependent switching speed is utilized for information processing. It enables the configuration of tailorable programming and forgetting times in response to the positive and negative driving voltage pulses. Benchmarking this framework on time-series prediction problems reveals that the configurable forgetting dynamics enables a high prediction accuracy using a rather small number of memristive input channels. The training is based either on optimizing the output layer using linear regression with fixed forgetting times, or on optimizing the forgetting times as well. In the first case, six memristive channels, while in the second, only two memristive channels are used to demonstrate excellent prediction accuracy for the benchmark tasks. This scheme allows for the tunability of the operating frequency over many orders of magnitude: by adjusting the input voltage levels, the information processing speed of the same memristive dynamic layer can be increased from the kHz to the MHz range while maintaining excellent prediction accuracy. These findings demonstrate the merits of memristor based dynamic networks in the analysis, prediction and recovery of fast temporal signals, approaching telecommunication data rates.
